Toward the goal of identifying complete sets of transcription factor (TF)-binding sites in the genomes of several gamma proteobacteria, and hence describing their transcription regulatory networks, we present a phylogenetic footprinting method for identifying these sites. Probable transcription regulatory sites upstream of Escherichia coli genes were identified by cross-species comparison using an extended Gibbs sampling algorithm. Close examination of a study set of 184 genes with documented transcription regulatory sites revealed that when orthologous data were available from at least two other gamma proteobacterial species, 81% of our predictions corresponded with the documented sites, and 67% corresponded when data from only one other species were available. That the remaining predictions included bona fide TF-binding sites was proven by affinity purification of a putative transcription factor (YijC) bound to such a site upstream of the fabA gene. Predicted regulatory sites for 2097 E.coli genes are available at http://www.wadsworth.org/resnres/bioinfo/.

Target prediction for animal microRNAs has been hindered by the small number of verified targets available for evaluating the accuracy of predicted microRNA:target interactions. Recently, a dataset of 3404 microRNA-associated mRNA transcripts was identified by immuno-precipitation (IP) of the RNA-induced silencing complex (RISC) components, AIN-1 and AIN-2. Analysis of this dataset reveals enrichment for defining characteristics of functional microRNA target interactions, including structural accessibility of target sequences, the total free energy of microRNA:target hybridization, and the topology of base-pairing to the 5’ seed region of the microRNA. These enriched characteristics form the basis for a quantitative microRNA target prediction method, mirWIP (microRNA targets by Weighting IP dataset parameters), that optimizes sensitivity to verified microRNA:target interactions and specificity to the AIN-IP dataset. The mirWIP method can capture all of the known conserved microRNA:mRNA target relationships in C. elegans at a lower false positive rate than the current standard methods.

As the number of sequenced genomes has grown, the questions of which species are most useful and how many genomes are sufficient for comparison have become increasingly important for comparative genomics studies. We have systematically addressed these questions with respect to phylogenetic footprinting of transcription factor (TF) binding sites in the ␥-proteobacteria, and have evaluated the statistical significance of our motif predictions. We used a study set of 166 Escherichia coli genes that have experimentally identified TF binding sites upstream of the gene, with orthologous data from nine additional ␥-proteobacteria for phylogenetic footprinting. Just three species were sufficient for ∼ 74.0% of the motif predictions to correspond to the experimentally reported E. coli sites, and important characteristics to consider when choosing species were phylogenetic distance, genome size, and natural habitat. We also performed simulations using randomized data to determine the critical maximum a posteriori probability (MAP) values for statistical significance of our motif predictions (P = 0.05). Approximately 60% of motif predictions containing sites from just three species had average MAP values above these critical MAP values. The inclusion of a species very closely related to E. coli increased the number of statistically significant motif predictions, despite substantially increasing the critical MAP value.

Prediction and validation of microRNA (miRNA) targets are essential for understanding functions of miRNAs in gene regulation. Crosslinking immunoprecipitation (CLIP) allows direct identification of a huge number of Argonaute-bound target sequences that contain miRNA binding sites. By analysing data from CLIP studies, we identified a comprehensive list of sequence, thermodynamic and target structure features that are essential for target binding by miRNAs in the 3′ untranslated region (3′ UTR), coding sequence (CDS) region and 5′ untranslated region (5′ UTR) of target messenger RNA (mRNA). The total energy of miRNA:target hybridization, a measure of target structural accessibility, is the only essential feature common for both seed and seedless sites in all three target regions. Furthermore, evolutionary conservation is an important discriminating feature for both seed and seedless sites. These features enabled us to develop novel statistical models for the predictions of both seed sites and broad classes of seedless sites. Through both intra-dataset validation and inter-dataset validation, our approach showed major improvements over established algorithms for predicting seed sites and a class of seedless sites. Furthermore, we observed good performance from cross-species validation, suggesting that our prediction framework can be valuable for broad application to other mammalian species and beyond. Transcriptome-wide binding site predictions enabled by our approach will greatly complement the available CLIP data, which only cover small fractions of transcriptomes and known miRNAs due to non-detectable levels of expression. Software and database tools based on the prediction models have been developed and are available through Sfold web server at http://sfold.wadsworth.org.